The big take-home? Sci-Hub is everywhere. Most papers are being downloaded from the developing world: The top 3 countries are India, China, and Iran. But the rich industrialized countries use Sci-Hub, too. A quarter of the downloads came from OECD nations, and some of the most intense download hotspots correspond to the campuses of universities in the US and Europe, which supposedly have the most comprehensive journal access.

But these data have many more stories to tell. How do the reading habits of researchers differ by city? What are the hottest research topics in Indonesia, Italy, Brazil? Do the research topics shift when the Sci-Hub night owls take over? My analysis indicates a bimodal distribution over the course of the day, with most locations surging around lunchtime, and the rest peaking at 1am local time. The animated map above shows just 2 days of the data.

Something everyone would like to know: What proportion of downloaded articles are actually unavailable from nearby university libraries? Put another way: What is the size of the knowledge gap that Sci-Hub is bridging?

A desire for a full understanding of the climate dynamics driven by natural processes versus human activities motivates much of the research on analyzing observations of climate and ocean changes. Comprehensive instrumental climate databases with high spatial and temporal resolution have been available on the global scale since the 1970s, but few time series cover more than a century. Proxy data (e.g., sediment, ice cores, and tree rings) provide information about events in the distant past. However, linking the instrumental records of climate and the paleoproxy time series remains a challenge.

Climatologists can use instruments to observe a long-term tendency (multidecadal to secular); however, because of the limited timescale covered, they are unable to draw conclusions on the driving mechanisms. Our “paleo–time series” workshop addressed this issue by discussing how the paleoclimatological community can help in understanding instrumentally observed long-term tendencies.

Another important question that participants raised concerned how to process the time series. Statistical techniques, together with times series analysis methods such as
continuous wavelet transform
, are useful for comparisons of the high-frequency variability in both instrumental and proxy records. These methods could also be applied for documenting the low-frequency variability captured in the
paleorecords
.

Coordinating Partners

Funding Partners

Led by the Center of International Forestry Research (CIFOR) alongside founding partners UN Environment and the World Bank, with core funding provided by the German Government, the Global Landscapes Forum (GLF) accelerates action towards the creation of more resilient, equitable, profitable, productive and healthy landscapes and the achievement of the UNFCCC Paris Agreement and Sustainable Development Goals (Agenda 2030).